"""
The WideResNet model (https://github.com/xternalz/WideResNet-pytorch/).
"""

import torch
import torch.nn as nn
import torch.nn.functional as F

from config import Config


class BasicBlock(nn.Module):
    def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
        super(BasicBlock, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv1 = nn.Conv2d(in_planes,
                               out_planes,
                               kernel_size=3,
                               stride=stride,
                               padding=1,
                               bias=False)
        self.bn2 = nn.BatchNorm2d(out_planes)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_planes,
                               out_planes,
                               kernel_size=3,
                               stride=1,
                               padding=1,
                               bias=False)
        self.droprate = dropRate
        self.equalInOut = (in_planes == out_planes)
        self.convShortcut = (not self.equalInOut) and nn.Conv2d(
            in_planes,
            out_planes,
            kernel_size=1,
            stride=stride,
            padding=0,
            bias=False) or None

    def forward(self, x):
        if not self.equalInOut:
            x = self.relu1(self.bn1(x))
        else:
            out = self.relu1(self.bn1(x))
        out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x)))
        if self.droprate > 0:
            out = F.dropout(out, p=self.droprate, training=self.training)
        out = self.conv2(out)
        return torch.add(x if self.equalInOut else self.convShortcut(x), out)


class NetworkBlock(nn.Module):
    def __init__(self,
                 nb_layers,
                 in_planes,
                 out_planes,
                 block,
                 stride,
                 dropRate=0.0):
        super(NetworkBlock, self).__init__()
        self.layer = self._make_layer(block, in_planes, out_planes, nb_layers,
                                      stride, dropRate)

    def _make_layer(self, block, in_planes, out_planes, nb_layers, stride,
                    dropRate):
        layers = []
        for i in range(int(nb_layers)):
            layers.append(
                block(i == 0 and in_planes or out_planes, out_planes,
                      i == 0 and stride or 1, dropRate))
        return nn.Sequential(*layers)

    def forward(self, x):
        return self.layer(x)


class Model(nn.Module):
    def __init__(self, depth, num_classes, widen_factor=1, dropRate=0.0):
        super().__init__()

        n_channels = [
            16, 16 * widen_factor, 32 * widen_factor, 64 * widen_factor
        ]
        assert (depth - 4) % 6 == 0
        n = (depth - 4) / 6
        block = BasicBlock

        # 1st conv before any network block
        self.conv1 = nn.Conv2d(3,
                               n_channels[0],
                               kernel_size=3,
                               stride=1,
                               padding=1,
                               bias=False)
        # 1st block
        self.block1 = NetworkBlock(n, n_channels[0], n_channels[1], block, 1,
                                   dropRate)
        # 2nd block
        self.block2 = NetworkBlock(n, n_channels[1], n_channels[2], block, 2,
                                   dropRate)
        # 3rd block
        self.block3 = NetworkBlock(n, n_channels[2], n_channels[3], block, 2,
                                   dropRate)
        # global average pooling and classifier
        self.bn1 = nn.BatchNorm2d(n_channels[3])
        self.relu = nn.ReLU(inplace=True)
        self.fc = nn.Linear(n_channels[3], num_classes)
        self.n_channels = n_channels[3]

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight,
                                        mode='fan_out',
                                        nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                m.bias.data.zero_()

    def forward(self, x):
        out = self.conv1(x)
        out = self.block1(out)
        out = self.block2(out)
        out = self.block3(out)
        out = self.relu(self.bn1(out))
        out = F.avg_pool2d(out, 8)
        out = out.view(-1, self.n_channels)
        return self.fc(out)

    @staticmethod
    def get_model(*args):
        """Obtaining an instance of this model. """
        return Model(Config().trainer.num_layers, Config().trainer.num_classes)
